Julia is a new Language, that is fast, high level, dynamic and optimized for Data Science. But due to its young age, it might not be for everyone yet. Learn about Julia's strengths and how you can integrate it in your Python workflow!
You have performance problems with your Python code but are sick of rewriting it in Cython/C/C++? Julia might just be the language to solve your problems.
It's ease of use rivals that of Python, but it runs as fast as C. It has a powerful, optional type system, which is great for writing high level and generic code.
In this talk I will walk you over some examples that show off Julia as a great language for writing math and complex libraries.
I will also explain, how one can wrap their Julia packages in Python, to also reach the people that are not yet ready to leave Python for a new, hip language.
Julia is a new Language, that is fast, high level, dynamic and optimized for Data Science. Learn about Julia's strengths and how you can integrate it in your Python workflow!
While studying Cognitive Science, Simon developed a great interest for Machine Learning and Computer Vision. During his one-year stay at the the Volkswagen Research lab in San Francisco, he was working on computer vision in C++. Looking for better alternatives to a cumbersome language like C++ or a slow language like Python got him interested in language design. This quickly led him to pick up Julia, where he supported work by the Julia MIT lab and authored a number of open source libraries for plotting, GPU acceleration and Machine Learning. Today, Simon is a researcher at Nextjournal, where he is responsible for making Julia easily accessible.